Search Results - Encoder-Decoder ConvLSTM

Self-Attention (SA)-ConvLSTM Encoder–Decoder Structure-Based Video Prediction for Dynamic Motion Estimation by Kim, Jeongdae, Choo, Hyunseung, Jeong, Jongpil

“… However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder…”
Get full text

EDDA-ConvLSTM: Encoder-Decoder Dual Attention ConvLSTM for Moroccan Coastal Sea Surface Temperature Prediction by Zahrae El Azhary, Fatima, Minaoui, Khalid

“…This study presents an advanced encoder-decoder dual attention convolutional long short-term memory (ConvLSTM…”
Get full text

An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting by Wang, Cheng, Xue, Kaiyu, Shi, Chuang

“… This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance…”
Get full text

Predicting 360° Video Saliency: A ConvLSTM Encoder-Decoder Network With Spatio-Temporal Consistency by Wan, Zhaolin, Qin, Han, Xiong, Ruiqin, Li, Zhiyang, Fan, Xiaopeng, Zhao, Debin

“… In this study, we propose a novel spatio-temporal consistency generative network for 360° VSP. A dual-stream encoder-decoder architecture is adopted to process the forward and backward frame sequences of 360…”
Get full text

Effective Multi-Step PM2.5 and PM10 Air Quality Forecasting Using Bidirectional ConvLSTM Encoder-Decoder With STA Mechanism by Lakshmi, S., Krishnamoorthy, A.

“…Effective prediction of PM2.5 and PM10 levels is essential for preserving public health and informing governmental actions. Nevertheless, the unpredictable…”
Get full text

PDED-ConvLSTM: Pyramid Dilated Deeper Encoder–Decoder Convolutional LSTM for Arctic Sea Ice Concentration Prediction by Zhang, Deyu, Wang, Changying, Huang, Baoxiang, Ren, Jing, Zhao, Junli, Hou, Guojia

“… To address these challenges, we propose an innovative encoder–decoder pyramid dilated convolutional long short-term memory network (DED-ConvLSTM…”
Get full text

Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis by Fan, Ming, Wang, Hongsheng, Zhang, Jing, Hosseini, Seyyed A., Lu, Dan

“… In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED…”
Get full text

ResMIBCU-Net: an encoder–decoder network with residual blocks, modified inverted residual block, and bi-directional ConvLSTM for impacted tooth segmentation in panoramic X-ray images by Imak, Andaç, Çelebi, Adalet, Polat, Onur, Türkoğlu, Muammer, Şengür, Abdulkadir

“…Objective Impacted tooth is a common problem that can occur at any age, causing tooth decay, root resorption, and pain in the later stages. In recent years,…”
Get full text

Forecasting Stock Market Using Machine Learning Approach Encoder-Decoder ConvLSTM by Iqbal, Khurum, Hassan, Ali, Hassan, Syed Shah Mir Ul, Iqbal, Shuaib, Aslam, Faheem, Mughal, Khurrum S

“… The goal of this study is to create a hybrid Deep Learning model (Encoder-Decoder ConvLSTM…”
Get full text

Prediction of maize growth stages based on deep learning by Yue, Yang, Li, Jin-Hai, Fan, Li-Feng, Zhang, Li-Li, Zhao, Peng-Fei, Zhou, Qiao, Wang, Nan, Wang, Zhong-Yi, Huang, Lan, Dong, Xue-Hui

“…•ConvLSTM encoder-decoder model can forecast daily weather factors correctly.•Hybrid model and data-driven model can predict maize growth stages…”
Get full text

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model by Xia, Guozhen, Zhang, Fubin, Wang, Cheng, Zhou, Chen

“…In this paper, we proposed an innovative encoder‐decoder structure with a convolution long short‐term memory (ED‐ConvLSTM…”
Get full text

Local and Long-range Convolutional LSTM Network: A novel multi-step wind speed prediction approach for modeling local and long-range spatial correlations based on ConvLSTM by Yu, Mei, Tao, Boan, Li, Xuewei, Liu, Zhiqiang, Xiong, Wei

“… Deformable Convolution V2 and Coordinate Attention for multi-step spatiotemporal wind speed prediction. A ConvLSTM encoder…”
Get full text

Multi-Scale Attention and Encoder-Decoder Network for Video Saliency Object Detection by Bi, Hongbo, Zhu, Huihui, Yang, Lina, Wu, Ranwan

“…— In recent years, video saliency object detection has received more and more attention, and many excellent algorithms have been proposed. In the paper, we…”
Get full text

Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM by Li, Liangchao, Liu, Haijun, Le, Huijun, Yuan, Jing, Shan, Weifeng, Han, Ying, Yuan, Guoming, Cui, Chunjie, Wang, Junling

“… Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules…”
Get full text

Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid by Dorado Rueda, Fernando, Durán Suárez, Jaime, del Real Torres, Alejandro

“… To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms…”
Get full text

New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast by de Paulo, M. C. M., Marques, H. A., Feitosa, R. Q., Ferreira, M. P.

“…) of the days before the prediction period. We proposed modifications to the encoder–decoder convolutional long short-term memory (ED-ConvLSTM…”
Get full text

Semantic segmentation of oblique UAV video based on ConvLSTM in complex urban area by Majidizadeh, Abbas, Hasani, Hadiseh, Jafari, Marzieh

“… . , require accurate and efficient segmentation algorithms. The proposed method implements a deep learning framework combining SegNet encoder-decoder architecture and convolutional long short-term memory (ConvLSTM…”
Get full text

ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction by Li, Yalan, Deng, Haiming, Xiao, Jian, Li, Bin, Han, Tao, Huang, Jianquan, Liu, Haijun

“… Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective…”
Get full text

Dual Convolutional LSTM Network for Referring Image Segmentation by Ye, Linwei, Liu, Zhi, Wang, Yang

“… Our model consists of an encoder network and a decoder network, where ConvLSTM is used in both encoder and decoder networks to capture spatial and sequential information…”
Get full text

Deep unsupervised multi-modal fusion network for detecting driver distraction by Zhang, Yuxin, Chen, Yiqiang, Gao, Chenlong

“…•A state-of-the-art, unsupervised, end-to-end method to detect driver distraction.•Different network architectures to perform embedding subnetworks for…”
Get full text